Perception of Artificial Intelligence in Spain

This paper analyses the perception of Artificial Intelligence (AI) by individuals in Spain and the factors associated with it. It uses data from 6308 individuals from a 2018 Spanish survey. A binary logit regression model is formulated and estimated for the attitude towards robots and AI and its possible determinants. As main results are that a gender gap is detected, and that people have a negative attitude if they are not interested in scientific discoveries and technological developments and if AI and robots are not useful at work.     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

Local scour at offshore windfarm monopile foundations: A review

In this article, current research findings of local scour at offshore windfarm monopile foundations are presented. The scour mechanisms and scour depth prediction formulas under different hydrodynamic conditions are summarized, including the current-only condition, wave-only condition, combined wave-current condition, and complex dynamic condition. Furthermore, this article analyzes the influencing factors on the basis of classical equations for predicting the equilibrium scour depth under specific conditions. The weakness of existing researches and future prospects are also discussed. It is suggested that future research shall focus on physical experiments under unsteady tidal currents or other complex loadings. The computational fluid dynamics-discrete element method and artificial intelligence technique are suggested being adopted to study the scour at offshore windfarm foundations.     

Zinc doped Fe2O3 for boosting Electrocatalytic Nitrogen Fixation to ammonia under mild conditions

Artificial nitrogen fixation is emerging as a promising approach for synthesis of ammonia at mild conditions. Inspired by biological nitrogen fixation based on bacteria containing iron, zinc doped Fe₂O₃ nanoparticles are proposed as an efficient and earth abundant electrocatalyst for converting N₂ to NH₃. In neutral media, it achieves a maximum Faradaic efficiency (FE) of 10.4% and a large NH₃ yield rate of 15.1 μg h^?1 mg^?1_cat. at ?0.5 V vs. reversible hydrogen electrode. This catalyst also exhibits excellent selectivity and stability. Theoretical calculations suggest the reaction follows the associative enzymatic mechanism and it has a barrier of as low as 0.68 eV.     

Historical and contemporary movement and survival rates of walleye (Sander vitreus) in Lake Winnipeg,Canada

Understanding patterns of fish movement in large lake ecosystems is essential for determining appropriate management actions as differences in movement behaviour can influence life history traits such as growth and survival. Lake Winnipeg in Manitoba, Canada supports the 2nd largest walleye (Sander vitreus) commercial fishery in North America. We used mark-recapture models to determine movement and estimate survival of walleye between basins of Lake Winnipeg in historical and contemporary contexts, comparing a tag-recovery study completed historically during 1974–1977 with a contemporary (2017–2019) acoustic telemetry study. Mark-recapture models revealed comparably low but detectable annual transitions between basins from historical (0.3–1.2%) and contemporary datasets (7–8.5%). Historically, fish > 300 mm more frequently moved in a south to north direction. Contemporary estimates suggest similar length-based directionality in that fish > 350 mm were always more likely to move in a south-north direction. Contemporary annual survival derived from mark-recapture models ranged between 27 and 45% and 64.3% when derived from catch curve analysis, while independently derived annual historical survival estimates ranged between 50 and 69% and 45.5% from catch curve analysis. Using the contemporary dataset, we also observed seasonal variation in movement and survival between basins, with the greatest movement across the lake occurring during the fall. Our results demonstrate a persisting pattern of low but measurable movement, suggesting between basin movement is not unusual for Lake Winnipeg. Further, low walleye survival rates reported here for the two time periods studied, support recent management actions to reduce fishing pressure across the lake.     

A state-of-the-art review on the elaboration of fish gelatin as bioactive packaging: Special emphasis on nanotechnology-based approaches

BackgroundThe last decade has noticed the expansion of green materials, which aims to reduce the human impact on the environment. Green polymers are clearly tendency subdivision of this stream and numerous bio-sourced plastics have been developed. Recent research has further focused on the development of new bio-based materials such as edible/biodegradable films for food products.Scope and approachFish gelatin (FG), a protein with recreatable reserve, biodegradability, and processability, has a remarkable potential in bio-packaging. However, there have been noticeable issues concerning the use of FG as packing material, including its low mechanical strength, poor moisture stability, and poor water barrier properties. This paper aims to review the state-of-the-art in development of FG-based films and highlights how they play a crucial role in modifying the properties of such films. Various types of nanofillers that have been included into FG to fabricate bio-nanocomposite films, such as nanoclays (montmorillonite, sepiolite), polysaccharide nanofillers (nanowhiskers/nanofibers), metal ions (silver, copper) and metal oxides nanoparticles (ZnO, TiO₂) are reviewed.Key findings and conclusionsCross-linking is a promising technique to improve the performance and applicability of FG-based biomaterials, particularly respecting their water sensitivity, which hinders many of their potential uses as food contact materials. Bio-nanocomposite technology may help to make high-performance materials with extra bio-functional properties, and it is anticipated to be a dynamic research in the future. In general, more research is required to ameliorate application processes of FG films, especially physical aspects, to be suitable for bio-packaging application.     

Identification of digital technologies and digitalisation trends in the mining industry

Digitalisation in mining refers to the use of computerised or digital devices or systems and digitised data that are to reduce costs, improve business productivity, and transform mining practices. However, it remains increasingly difficult for mining companies to decide which digital technologies are most relevant to their needs and individual mines. This paper provides an overview of digital technologies currently relevant to mining companies as presented and discussed by mining journals, the media and insight reports of leading consultancy agencies. Relevant technologies were systematically identified using text-mining techniques, and network analyses established the relations between significant technologies. Results demonstrated that currently 107 different digital technologies are pursued in the mining sector. Also, an analysis of the actual implementation of digital technologies in 158 active surface and underground mines reveals a limited uptake of digital technologies in general and that the uptake increases with the run-of-mine production. Large-scale mining operations appear to select and apply digital technologies suitable to their needs, whereas operations with lower production rates do not implement the currently available digital technologies to the same extent. These minor producers may require other digital transformation solutions tailored to their capabilities and needs and applicable to their scale of operations.     

A datamining approach to classify,select and predict the formation enthalpy for intermetallic compound hydrides

In this paper, two techniques of datamining tools were adopted, a principal component analysis (PCA) and artificial neural network (ANN). A PCA to classify, select and identify several combinations between transition element A and B (B = Ti, Zr, Hf, Sc, Y, La and Th) and ANN to predict ΔH for ternary hydrides. Based on the datasets selected from different works, a principal component analysis (PCA) has been applied to select, classify and identify around 76 possible combinations between transition metal elements A and B. The results showed that the clustering of combinations A-B are significantly influenced by the atomic parameters of element A, such atomic radius (R_A), Pauling's electronegativity (χ_A) and atomic electron density (Z_A/R_A³). From 76 combinations, 55 systems which have χ_A ≥ 1.5, Z_A/R_A³>1.28 and R_A < 1.46 Å are categorized as group 1. On the other hand, 21 systems which have χ_A < 1.5, Z_A/R_A³ < 1.28, and R_A > 1.46 Å are categorized as group 2. From the first group, 46 different combinations are identified and have a negative ΔH, within 18 well-known promising binary alloys of hydrogen storage.An (6-15-1) architecture of artificial neural network (ANN) has been developed to estimate the ΔH for the other ternary hydrides selected from different published works. The performance indices such as relative error, coefficient of determination (R²) and mean square error (MSE) were used to control the performance of obtained results. In addition to this, the ΔH obtained from ANN model were compared with those experimental data and theoretical results available in the literature.     

Coupling in situ experiments and modeling – Opportunities for data fusion,machine learning,and discovery of emergent behavior

This paper reviews recent studies, that not only includes both experiments and modeling components, but celebrates a close coupling between these techniques, in order to provide insights into the plasticity and failure of polycrystalline metals. Examples are provided of studies across multiple-scales, including, but not limited to, density functional theory combined with atom probe tomography, molecular dynamics combined with in situ transmission electron miscopy, discrete dislocation dynamics combined with nanopillars experiments, crystal plasticity combined with digital image correlation, and crystal plasticity combined with in situ high energy X-ray diffraction. The close synergy between in situ experiments and modeling provides new opportunities for model calibration, verification, and validation, by providing direct means of comparison, thus removing aspects of epistemic uncertainty in the approach. Further, data fusion between in situ experimental and model-based data, along with data driven approaches, provides a paradigm shift for determining the emergent behavior of deformation and failure, which is the foundation that underpins the mechanical behavior of polycrystalline materials.